The Microflow Cytometer

2019 ◽  
Author(s):  
Frances S. Ligler ◽  
Jason S. Kim
Keyword(s):  
Microflow Cytometer

CMOS Microflow Cytometer for Magnetic Label Detection and Classification

2017 ◽  
Vol 52 (2) ◽  
pp. 543-555 ◽  
Author(s):  
Pramod Murali ◽  
Ali M. Niknejad ◽  
Bernhard E. Boser
Keyword(s):  
Microflow Cytometer ◽  
Magnetic Label

Microflow Cytometer Electronics

2010 ◽  
Author(s):  
Jeffrey Erickson ◽  
Dustin Kreft ◽  
Matthew Kniller
Keyword(s):  
Microflow Cytometer

scholarly journals Handheld Microflow Cytometer Based on a Motorized Smart Pipette, a Microfluidic Cell Concentrator, and a Miniaturized Fluorescence Microscope

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2761 ◽  
Author(s):  
Byeongyeon Kim ◽  
Dayoung Kang ◽  
Sungyoung Choi
Keyword(s):  
Flow Cytometry ◽  
High Throughput ◽  
Flow Cytometric Analysis ◽  
Sample Volume ◽  
Fluorescence Microscope ◽  
Comprehensive Approach ◽  
Optical Systems ◽  
Simple Protocol ◽  
Microflow Cytometer ◽  
On Chip

Miniaturizing flow cytometry requires a comprehensive approach to redesigning the conventional fluidic and optical systems to have a small footprint and simple usage and to enable rapid cell analysis. Microfluidic methods have addressed some challenges in limiting the realization of microflow cytometry, but most microfluidics-based flow cytometry techniques still rely on bulky equipment (e.g., high-precision syringe pumps and bench-top microscopes). Here, we describe a comprehensive approach that achieves high-throughput white blood cell (WBC) counting in a portable and handheld manner, thereby allowing the complete miniaturization of flow cytometry. Our approach integrates three major components: a motorized smart pipette for accurate volume metering and controllable liquid pumping, a microfluidic cell concentrator for target cell enrichment, and a miniaturized fluorescence microscope for portable flow cytometric analysis. We first validated the capability of each component by precisely metering various fluid samples and controlling flow rates in a range from 219.5 to 840.5 μL/min, achieving high sample-volume reduction via on-chip WBC enrichment, and successfully counting single WBCs flowing through a region of interrogation. We synergistically combined the three major components to create a handheld, integrated microflow cytometer and operated it with a simple protocol of drawing up a blood sample via pipetting and injecting the sample into the microfluidic concentrator by powering the motorized smart pipette. We then demonstrated the utility of the microflow cytometer as a quality control means for leukoreduced blood products, quantitatively analyzing residual WBCs (rWBCs) in blood samples present at concentrations as low as 0.1 rWBCs/μL. These portable, controllable, high-throughput, and quantitative microflow cytometric technologies provide promising ways of miniaturizing flow cytometry.

scholarly journals Machine learning issues and opportunities in ultrafast particle classification for label-free microflow cytometry

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alessio Lugnan ◽  
Emmanuel Gooskens ◽  
Jeremy Vatin ◽  
Joni Dambre ◽  
Peter Bienstman
Keyword(s):  
Machine Learning ◽  
Computational Cost ◽  
Particle Analysis ◽  
Label Free ◽  
Machine Learning Approach ◽  
Microflow Cytometer ◽  
Learning Machine ◽  
Learning Issues ◽  
Low Computational Cost

AbstractMachine learning offers promising solutions for high-throughput single-particle analysis in label-free imaging microflow cytomtery. However, the throughput of online operations such as cell sorting is often limited by the large computational cost of the image analysis while offline operations may require the storage of an exceedingly large amount of data. Moreover, the training of machine learning systems can be easily biased by slight drifts of the measurement conditions, giving rise to a significant but difficult to detect degradation of the learned operations. We propose a simple and versatile machine learning approach to perform microparticle classification at an extremely low computational cost, showing good generalization over large variations in particle position. We present proof-of-principle classification of interference patterns projected by flowing transparent PMMA microbeads with diameters of $${15.2}\,\upmu \text {m}$$ 15.2 μ m and $${18.6}\,\upmu \text {m}$$ 18.6 μ m . To this end, a simple, cheap and compact label-free microflow cytometer is employed. We also discuss in detail the detection and prevention of machine learning bias in training and testing due to slight drifts of the measurement conditions. Moreover, we investigate the implications of modifying the projected particle pattern by means of a diffraction grating, in the context of optical extreme learning machine implementations.

scholarly journals Optofluidic characterization of marine algae using a microflow cytometer

2011 ◽  
Vol 5 (3) ◽  
pp. 032009 ◽  
Author(s):  
Nastaran Hashemi ◽  
Jeffrey S. Erickson ◽  
Joel P. Golden ◽  
Frances S. Ligler
Keyword(s):  
Marine Algae ◽  
Microflow Cytometer

scholarly journals A hard microflow cytometer using groove-generated sheath flow for multiplexed bead and cell assays

2010 ◽  
Vol 398 (5) ◽  
pp. 1871-1881 ◽  
Author(s):  
Abel L. Thangawng ◽  
Jason S. Kim ◽  
Joel P. Golden ◽  
George P. Anderson ◽  
Kelly L. Robertson ◽  
...  
Keyword(s):  
Sheath Flow ◽  
Cell Assays ◽  
Microflow Cytometer

scholarly journals Multiplexed Detection of Bacteria and Toxins Using a Microflow Cytometer

2009 ◽  
Vol 81 (13) ◽  
pp. 5426-5432 ◽  
Author(s):  
Jason S. Kim ◽  
George P. Anderson ◽  
Jeffrey S. Erickson ◽  
Joel P. Golden ◽  
Mansoor Nasir ◽  
...  
Keyword(s):  
Multiplexed Detection ◽  
Microflow Cytometer

Particles small angle forward-scattered light measurement based on photovoltaic cell microflow cytometer

2013 ◽  
Vol 35 (2-3) ◽  
pp. 337-344 ◽  
Author(s):  
Han-Taw Chen ◽  
Lung-Ming Fu ◽  
Hsing-Hui Huang ◽  
Wei-En Shu ◽  
Yao-Nan Wang
Keyword(s):  
Small Angle ◽  
Scattered Light ◽  
Photovoltaic Cell ◽  
Light Measurement ◽  
Microflow Cytometer
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